Detecting differences in gene expression is an important part of single-cell RNA sequencing experiments, and many statistical methods have been developed for this aim. Most differential expression analyses focus on comparing expression between two groups (e.g., treatment vs. control). But there is increasing interest in multi-condition differential expression analyses in which expression is measured in many conditions, and the aim is to accurately detect and estimate expression differences in all conditions. We show that directly modeling single-cell RNA-seq counts in all conditions simultaneously, while also inferring how expression differences are shared across conditions, leads to greatly improved performance for detecting and estimating expression differences compared to existing methods. We illustrate the potential of this new approach by analyzing data from a single-cell experiment studying the effects of cytokine stimulation on gene expression. We call our new method "Poisson multivariate adaptive shrinkage", and it is implemented in an R package available online at https://github.com/stephenslab/poisson.mash.alpha.

翻译：检测基因表达差异是单细胞RNA测序实验的重要环节，为此已开发出多种统计方法。大多数差异表达分析侧重于比较两组间的表达差异（如处理组与对照组）。然而，对多条件差异表达分析的需求日益增长——即在多种条件下测量表达水平，旨在准确检测并估计所有条件下的表达差异。我们证明，相较于现有方法，同时直接建模所有条件下的单细胞RNA-seq计数，并推断表达差异在不同条件间的共享模式，可显著提升表达差异检测与估计的性能。通过分析一项研究细胞因子刺激对基因表达影响的单细胞实验数据，我们展示了这种新方法的潜力。我们将该方法命名为"泊松多元自适应收缩"，其实现以R包形式发布，可通过https://github.com/stephenslab/poisson.mash.alpha获取。